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%遗传算法 VRP 问题 Matlab实现 
 
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clear;clc 
load matlab.mat 
Sup_Parent=[3 6 7 17 18 5 8 16 19 15 26 25 4 27 29 2 9 14 20 28 30 31 13 22 21 24 11 12 23 10]-1; 
G=100;%种群大小 
Parent=rand(G,30);%随即父代 
for i=1:G 
[m n]=sort(Parent(i,:));                                                                                       %初始化数据 
Parent(i,:)=n; 
end 
Pc=0.8;%交叉比率 
Pm=0.2;%变异比率 
species=[Sup_Parent;Parent];%种群 
children=[];%子代 
%fitness_value(4070,1)=0;%适应值 
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g=input('更新时代次数'); 
for generation=1:g  
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Parent=species;%子代变成父代 
children=[];%子代 
%选择交叉父代 
[n m]=size(Parent); 
% select=rand(1,n)<Pc; 
% select=find(select==1); 
                                                                                                                %交叉 
for i=1:n 
    for j=i:n 
        if i~=j & rand<Pc 
            jiaocha 
        end 
    end 
end 
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for i=1:n 
    if rand<Pm 
    parent=Parent(i,:);%变异个体 
    X=floor(rand*30)+1; 
    Y=floor(rand*30)+1; 
    Z=parent(X); 
    parent(X)=parent(Y); 
    parent(Y)=Z;                                                                                                 %变异 
    children=[children;parent]; 
    end 
end 
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%计算子代适应值 
[m n]=size(children); 
fitness_value_c=zeros(m,1);%子代适应值 
for i=1:m 
    l1=1; 
    for l2=1:n 
        if sum(data(children(i,l1:l2),3))>25 
            fitness_c 
            l1=l2; 
        end 
        if l2==n 
            l2=l2+1; 
            fitness_c 
        end 
                                                                                                                  %计算适应值 
         
     
    end 
end 
%计算父代适应值 
[m n]=size(Parent); 
fitness_value_P=zeros(m,1);%父代适应值 
for i=1:m 
    l1=1; 
    for l2=1:n 
        if sum(data(Parent(i,l1:l2),3))>25 
            fitness_P 
            l1=l2; 
        end 
        if l2==n 
            l2=l2+1; 
            fitness_P 
        end 
    end 
end 
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%淘汰子代 
[m n]=sort(fitness_value_c); 
children=children(n(1:G),:); 
fitness_value_c=fitness_value_c(n(1:G)); 
%淘汰父代 
[m n]=sort(fitness_value_P); 
Parent=Parent(n(1:G),:); 
fitness_value_P=fitness_value_P(n(1:G)); 
%淘汰种群 
species=[children;Parent]; 
fitness_value=[fitness_value_c;fitness_value_P]; 
[m n]=sort(fitness_value); 
species=species(n(1:G),:); 
fitness_value=fitness_value(n(1:G));                                                                             %更新世代 
 
 
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end 
species(1,:)%最优线路 
fitness_value(1)%最优费用